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15 Math Concepts Every Data Scientist Should Know
15 Math Concepts Every Data Scientist Should Know

15 Math Concepts Every Data Scientist Should Know: Understand and learn how to apply the math behind data science algorithms

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Profile Icon David Hoyle
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Can$40.99 Can$45.99
eBook Aug 2024 510 pages 1st Edition
eBook
Can$40.99 Can$45.99
Paperback
Can$56.99
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Arrow left icon
Profile Icon David Hoyle
Arrow right icon
Can$40.99 Can$45.99
eBook Aug 2024 510 pages 1st Edition
eBook
Can$40.99 Can$45.99
Paperback
Can$56.99
Subscription
Free Trial
eBook
Can$40.99 Can$45.99
Paperback
Can$56.99
Subscription
Free Trial

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15 Math Concepts Every Data Scientist Should Know

Part 1: Essential Concepts

In this part, we will introduce the math concepts that you will encounter again and again as a data scientist. These concepts are vital to gain a good understanding of. After a recap of basic math notation, we look at the concepts related to how data is produced and then move through to concepts related to how to transform data, finally building up to our end goal of how to model data. These concepts are essential because you will use and combine them simultaneously in your work. By the end of Part 1, you will be comfortable with the math concepts that underpin almost all data science models and algorithms.

This section contains the following chapters:

  • Chapter 1, Recap of Mathematical Notation and Terminology
  • Chapter 2, Random Variables and Probability Distributions
  • Chapter 3, Matrices and Linear Algebra
  • Chapter 4, Loss Functions and Optimization
  • Anchor 5, Probabilistic Modeling
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Key benefits

  • Understand key data science algorithms with Python-based examples
  • Increase the impact of your data science solutions by learning how to apply existing algorithms
  • Take your data science solutions to the next level by learning how to create new algorithms
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

Data science combines the power of data with the rigor of scientific methodology, with mathematics providing the tools and frameworks for analysis, algorithm development, and deriving insights. As machine learning algorithms become increasingly complex, a solid grounding in math is crucial for data scientists. David Hoyle, with over 30 years of experience in statistical and mathematical modeling, brings unparalleled industrial expertise to this book, drawing from his work in building predictive models for the world's largest retailers. Encompassing 15 crucial concepts, this book covers a spectrum of mathematical techniques to help you understand a vast range of data science algorithms and applications. Starting with essential foundational concepts, such as random variables and probability distributions, you’ll learn why data varies, and explore matrices and linear algebra to transform that data. Building upon this foundation, the book spans general intermediate concepts, such as model complexity and network analysis, as well as advanced concepts such as kernel-based learning and information theory. Each concept is illustrated with Python code snippets demonstrating their practical application to solve problems. By the end of the book, you’ll have the confidence to apply key mathematical concepts to your data science challenges.

Who is this book for?

This book is for data scientists, machine learning engineers, and data analysts who already use data science tools and libraries but want to learn more about the underlying math. Whether you’re looking to build upon the math you already know, or need insights into when and how to adopt tools and libraries to your data science problem, this book is for you. Organized into essential, general, and selected concepts, this book is for both practitioners just starting out on their data science journey and experienced data scientists.

What you will learn

  • Master foundational concepts that underpin all data science applications
  • Use advanced techniques to elevate your data science proficiency
  • Apply data science concepts to solve real-world data science challenges
  • Implement the NumPy, SciPy, and scikit-learn concepts in Python
  • Build predictive machine learning models with mathematical concepts
  • Gain expertise in Bayesian non-parametric methods for advanced probabilistic modeling
  • Acquire mathematical skills tailored for time-series and network data types

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Aug 16, 2024
Length: 510 pages
Edition : 1st
Language : English
ISBN-13 : 9781837631940
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Languages :
Concepts :

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Product Details

Publication date : Aug 16, 2024
Length: 510 pages
Edition : 1st
Language : English
ISBN-13 : 9781837631940
Category :
Languages :
Concepts :

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Table of Contents

20 Chapters
Part 1: Essential Concepts Chevron down icon Chevron up icon
Chapter 1: Recap of Mathematical Notation and Terminology Chevron down icon Chevron up icon
Chapter 2: Random Variables and Probability Distributions Chevron down icon Chevron up icon
Chapter 3: Matrices and Linear Algebra Chevron down icon Chevron up icon
Chapter 4: Loss Functions and Optimization Chevron down icon Chevron up icon
Chapter 5: Probabilistic Modeling Chevron down icon Chevron up icon
Part 2: Intermediate Concepts Chevron down icon Chevron up icon
Chapter 6: Time Series and Forecasting Chevron down icon Chevron up icon
Chapter 7: Hypothesis Testing Chevron down icon Chevron up icon
Chapter 8: Model Complexity Chevron down icon Chevron up icon
Chapter 9: Function Decomposition Chevron down icon Chevron up icon
Chapter 10: Network Analysis Chevron down icon Chevron up icon
Part 3: Selected Advanced Concepts Chevron down icon Chevron up icon
Chapter 11: Dynamical Systems Chevron down icon Chevron up icon
Chapter 12: Kernel Methods Chevron down icon Chevron up icon
Chapter 13: Information Theory Chevron down icon Chevron up icon
Chapter 14: Non-Parametric Bayesian Methods Chevron down icon Chevron up icon
Chapter 15: Random Matrices Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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